IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing

2026-09-05

AI Summary

为解决无线感知中环境识别的领域迁移问题，提出结合IIns-VAE与最小最大风险分类器的IIns-VAE+框架，提升模型在不同场景下的适应性和鲁棒性。

Abstract

Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situational awareness. However, deep learning (DL) models for this task often fail to generalize under domain shift across diverse environments. While the Inter-Instance Variational Auto-encoder (IIns-VAE) learns features of rich representation, its neural classifier remains vulnerable to these distribution changes. In this paper, we propose IIns-VAE+, a hybrid model that combines the IIns-VAE framework with Minimax Risk Classifiers (MRC) to improve adaptability in transfer learning scenarios. We use real-world datasets to evaluate our framework across three transfer learning scenarios, including general to specific room environments, high to low label resolutions, and mixed to specific environments. The experimental results indicate that IIns-VAE+ significantly outperforms baselines, demonstrating its critical value in building adaptable and robust perceptive networks in future 6G systems.

Problem

• Environmental Identification
• Wireless Sensing
• Transfer Learning
• Domain Shift

Innovation

• IIns-VAE+
• Minimax Risk Classifiers
• transfer learning
• domain shift
• wireless sensing